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Endometrium assessment via thickness measurement is commonly performed in routine gynecological ultrasound examination for assessing the reproductive health of patients undergoing fertility related treatments and endometrium cancer screening in women with post-menopausal bleeding. This paper introduces a fully automated technique for endometrium thickness measurement from three-dimensional transvaginal...
This work introduces a novel artificial intelligence approach to household object recognition. The approach used in this work is feature-based and it works toward recognition under a broad range of circumstances. The necessary image processing techniques are applied to recognize the objects. These techniques include removal of shadow that is segmenting the object from its shadow, extraction of shape...
We present a histogram-based real-time solution to detecting directly irradiated regions in digital fluoroscopic images. Our method leverages the power of model matching, machine learning and domain knowledge to characterize and segment images using histograms. The input image is automatically identified as containing partial, all, or null direct radiation. The regions with direct radiation are segmented...
Glaucoma is one of the leading cause of blindness. The manual examination of optic cup and disc is a standard procedure used for detecting glaucoma. This paper presents a fully automatic regression based method which accurately segments optic cup and disc in retinal colour fundus image. First, we roughly segment optic disc using circular hough transform. The approximated optic disc is then used to...
Lung segmentation is the premise of the computer aided by lung disease. Lesions (such as mass and inflammation) lead to loss of large part of normal tissue in the lung region. They are close to the surrounding tissues (such as chest wall and blood vessel) and have similar CT values with surrounding tissues. So the segmentation method based on local feature cannot get the correct results and ASM model...
In this paper, we proposed a segmentation approach that not only segment an interest object but also label different semantic parts of the object, where a discriminative model is presented to describe an object in real world images as multiply, disparate and correlative parts. We propose a multi-stage segmentation approach to make inference on the segments of an object. Then we train it under the...
Level set-based algorithms have been extensively used for medical image segmentation. Despite their relative success, standard level set segmentations tend to fail when images are severely corrupted or in poorly defined regions. This problem has been tackled incorporating shape prior knowledge, i.e. restricting the evolving curve to a distribution of shapes pre-defined during a training process. Such...
Accurate and automatic detection and delineation of cervical cells are two critical precursor steps to automatic Pap smear image analysis and detecting pre-cancerous changes in the uterine cervix. To overcome noise and cell occlusion, many segmentation methods resort to incorporating shape priors, mostly enforcing elliptical shapes (e.g. [1]). However, elliptical shapes do not accurately model cervical...
This paper presents an accurate object segmentation method using novel active shape and appearance models that evolve according to the output of a support vector machine as well as traditional appearance features at shape landmarks. The method consists of two main processes including the building of the shape and appearance models and support vector machine (SVM) classifier, and the segmentation of...
It is an accepted fact that the challenge in applying image analysis to agriculture processes primarily lies in designing robust image analysis algorithms capable of coping with the combination of an unstructured environment and the inherent variability of biological objects. An example of such a challenging agriculture process could be the discrimination between crops and weed plants from images...
In orthopedics, trigger finger is one of the popular occupational hazards in recent years. Ultrasound images are usually used for diagnosing the severity of trigger finger clinically. Finger ultrasound image has two important characteristics: the shape of tendon is close to an ellipse, and the tendon boundaries vary significantly in image appearance. The traditional segmentation methods usually cannot...
Automated 3D image segmentation and classification of biological structures is a critical task in modern cellular and developmental biology. Furthermore new emerging acquisition systems, like light-sheet microscopy, permit to observe whole embryo or developing cells in 4D, allowing us to better understand the spatial organization of tissues and cells. Numerous algorithms have been developed for 3D...
This paper investigates a novel solution for the recognition of objects of interest in aerial images. The solution builds on a combination of algorithms inspired from the human visual system with classical and modern algorithms. The goal is to achieve intelligent and powerful approaches that allow for fast and automatic treatment of complex images. The methodology that is proposed innovatively combines...
Automated bone segmentation is one of the most challenging problems in medical imaging. The increasingly demanded MR imaging suffers from low contrast and signal-to-noise ratio when it comes to bones. To increase the segmentation robustness, a prior model of the structure could guide the segmentation when explicit information is missing or weakly presented. Statistical Shape Models (SSMs) are efficient...
We present a fully automatic model based system for segmenting bone MR images of the knee. The segmentation method is based on a fast Active Appearance Models (AAM) based on canonical correlation analysis algorithm (CCA-AAM) where the dependency between texture residuals and model parameters are estimated in fast manner. The model is built from manually segmented examples from the knee images. The...
This paper presents a new method for segmentation of ambiguously defined structures, such as the hippocampus, by exploiting prior knowledge from another perspective. An expert's experience of where to use prior knowledge and where image information, is captured as a local weighting map. This map can be used to locally guide the evolution in a level set evolution framework. Such a map is produced for...
This paper presents a novel approach to automatically segment the prostate (including seminal vesicles) using a surface that is actively deformed via shape and gray level models. The surface deformation process utilises the results of a multi-atlas registration approach, where training images are matched to the case image via non-rigid registration. Normalised mutual information is then used to measure...
In this paper, we present a template based approach to the segmentation of touching components in handwritten text lines. Local patches around touching components are identified and a dictionary is created consisting of template patches together with their correct segmentations. We use two shape context based methods to compute similarity between input patches and dictionary templates to find the...
Relative positioning between components of a structured object plays a key role for its interpretation. Fuzzy relative positioning templates are a description framework for 2D handwritten patterns, that is based on positioning models specifically designed for dealing with variability and imprecision of handwriting. In this work, we present fuzzy positioning templates and investigate the idea of recognizing...
In this paper we present a method to combine the detection and segmentation of object categories from 3D scenes. In the process, we combine the top-down cues available from object detection technique of Implicit Shape Models and the bottom-up power of Markov Random Fields for the purpose of segmentation. While such approaches have been tried for the 2D image problem domain before, this is the first...
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